diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 993583e2f4119..3073d489bad4a 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -338,6 +338,7 @@ def contains_file(self, filename):
     python_test_goals=[
         "pyspark.ml.feature",
         "pyspark.ml.classification",
+        "pyspark.ml.clustering",
         "pyspark.ml.recommendation",
         "pyspark.ml.regression",
         "pyspark.ml.tuning",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
new file mode 100644
index 0000000000000..dc192add6ca13
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.clustering
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.{Param, Params, IntParam, DoubleParam, ParamMap}
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans, KMeansModel => MLlibKMeansModel}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.{IntegerType, StructType}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.util.Utils
+
+
+/**
+ * Common params for KMeans and KMeansModel
+ */
+private[clustering] trait KMeansParams
+    extends Params with HasMaxIter with HasFeaturesCol with HasSeed with HasPredictionCol {
+
+  /**
+   * Set the number of clusters to create (k). Must be > 1. Default: 2.
+   * @group param
+   */
+  final val k = new IntParam(this, "k", "number of clusters to create", (x: Int) => x > 1)
+
+  /** @group getParam */
+  def getK: Int = $(k)
+
+  /**
+   * Param the number of runs of the algorithm to execute in parallel. We initialize the algorithm
+   * this many times with random starting conditions (configured by the initialization mode), then
+   * return the best clustering found over any run. Must be >= 1. Default: 1.
+   * @group param
+   */
+  final val runs = new IntParam(this, "runs",
+    "number of runs of the algorithm to execute in parallel", (value: Int) => value >= 1)
+
+  /** @group getParam */
+  def getRuns: Int = $(runs)
+
+  /**
+   * Param the distance threshold within which we've consider centers to have converged.
+   * If all centers move less than this Euclidean distance, we stop iterating one run.
+   * Must be >= 0.0. Default: 1e-4
+   * @group param
+   */
+  final val epsilon = new DoubleParam(this, "epsilon",
+    "distance threshold within which we've consider centers to have converge",
+    (value: Double) => value >= 0.0)
+
+  /** @group getParam */
+  def getEpsilon: Double = $(epsilon)
+
+  /**
+   * Param for the initialization algorithm. This can be either "random" to choose random points as
+   * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
+   * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
+   * @group expertParam
+   */
+  final val initMode = new Param[String](this, "initMode", "initialization algorithm",
+    (value: String) => MLlibKMeans.validateInitMode(value))
+
+  /** @group expertGetParam */
+  def getInitMode: String = $(initMode)
+
+  /**
+   * Param for the number of steps for the k-means|| initialization mode. This is an advanced
+   * setting -- the default of 5 is almost always enough. Must be > 0. Default: 5.
+   * @group expertParam
+   */
+  final val initSteps = new IntParam(this, "initSteps", "number of steps for k-means||",
+    (value: Int) => value > 0)
+
+  /** @group expertGetParam */
+  def getInitSteps: Int = $(initSteps)
+
+  /**
+   * Validates and transforms the input schema.
+   * @param schema input schema
+   * @return output schema
+   */
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
+    SchemaUtils.appendColumn(schema, $(predictionCol), IntegerType)
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by KMeans.
+ *
+ * @param parentModel a model trained by spark.mllib.clustering.KMeans.
+ */
+@Experimental
+class KMeansModel private[ml] (
+    override val uid: String,
+    private val parentModel: MLlibKMeansModel) extends Model[KMeansModel] with KMeansParams {
+
+  override def copy(extra: ParamMap): KMeansModel = {
+    val copied = new KMeansModel(uid, parentModel)
+    copyValues(copied, extra)
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val predictUDF = udf((vector: Vector) => predict(vector))
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
+
+  def clusterCenters: Array[Vector] = parentModel.clusterCenters
+}
+
+/**
+ * :: Experimental ::
+ * K-means clustering with support for multiple parallel runs and a k-means++ like initialization
+ * mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
+ * they are executed together with joint passes over the data for efficiency.
+ */
+@Experimental
+class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMeansParams {
+
+  setDefault(
+    k -> 2,
+    maxIter -> 20,
+    runs -> 1,
+    initMode -> MLlibKMeans.K_MEANS_PARALLEL,
+    initSteps -> 5,
+    epsilon -> 1e-4)
+
+  override def copy(extra: ParamMap): KMeans = defaultCopy(extra)
+
+  def this() = this(Identifiable.randomUID("kmeans"))
+
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
+
+  /** @group setParam */
+  def setK(value: Int): this.type = set(k, value)
+
+  /** @group expertSetParam */
+  def setInitMode(value: String): this.type = set(initMode, value)
+
+  /** @group expertSetParam */
+  def setInitSteps(value: Int): this.type = set(initSteps, value)
+
+  /** @group setParam */
+  def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+  /** @group setParam */
+  def setRuns(value: Int): this.type = set(runs, value)
+
+  /** @group setParam */
+  def setEpsilon(value: Double): this.type = set(epsilon, value)
+
+  /** @group setParam */
+  def setSeed(value: Long): this.type = set(seed, value)
+
+  override def fit(dataset: DataFrame): KMeansModel = {
+    val rdd = dataset.select(col($(featuresCol))).map { case Row(point: Vector) => point }
+
+    val algo = new MLlibKMeans()
+      .setK($(k))
+      .setInitializationMode($(initMode))
+      .setInitializationSteps($(initSteps))
+      .setMaxIterations($(maxIter))
+      .setSeed($(seed))
+      .setEpsilon($(epsilon))
+      .setRuns($(runs))
+    val parentModel = algo.run(rdd)
+    val model = new KMeansModel(uid, parentModel)
+    copyValues(model)
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+}
+
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 68297130a7b03..0a65403f4ec95 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -85,9 +85,7 @@ class KMeans private (
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
    */
   def setInitializationMode(initializationMode: String): this.type = {
-    if (initializationMode != KMeans.RANDOM && initializationMode != KMeans.K_MEANS_PARALLEL) {
-      throw new IllegalArgumentException("Invalid initialization mode: " + initializationMode)
-    }
+    KMeans.validateInitMode(initializationMode)
     this.initializationMode = initializationMode
     this
   }
@@ -550,6 +548,14 @@ object KMeans {
       v2: VectorWithNorm): Double = {
     MLUtils.fastSquaredDistance(v1.vector, v1.norm, v2.vector, v2.norm)
   }
+
+  private[spark] def validateInitMode(initMode: String): Boolean = {
+    initMode match {
+      case KMeans.RANDOM => true
+      case KMeans.K_MEANS_PARALLEL => true
+      case _ => false
+    }
+  }
 }
 
 /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
new file mode 100644
index 0000000000000..d09fa7fd5637c
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/clustering/JavaKMeansSuite.java
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.clustering;
+
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.List;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+public class JavaKMeansSuite implements Serializable {
+
+  private transient int k = 5;
+  private transient JavaSparkContext sc;
+  private transient DataFrame dataset;
+  private transient SQLContext sql;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaKMeansSuite");
+    sql = new SQLContext(sc);
+
+    dataset = KMeansSuite.generateKMeansData(sql, 50, 3, k);
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void fitAndTransform() {
+    KMeans kmeans = new KMeans().setK(k).setSeed(1);
+    KMeansModel model = kmeans.fit(dataset);
+
+    Vector[] centers = model.clusterCenters();
+    assertEquals(k, centers.length);
+
+    DataFrame transformed = model.transform(dataset);
+    List<String> columns = Arrays.asList(transformed.columns());
+    List<String> expectedColumns = Arrays.asList("features", "prediction");
+    for (String column: expectedColumns) {
+      assertTrue(columns.contains(column));
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
new file mode 100644
index 0000000000000..1f15ac02f4008
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
@@ -0,0 +1,114 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.clustering
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, SQLContext}
+
+private[clustering] case class TestRow(features: Vector)
+
+object KMeansSuite {
+  def generateKMeansData(sql: SQLContext, rows: Int, dim: Int, k: Int): DataFrame = {
+    val sc = sql.sparkContext
+    val rdd = sc.parallelize(1 to rows).map(i => Vectors.dense(Array.fill(dim)((i % k).toDouble)))
+      .map(v => new TestRow(v))
+    sql.createDataFrame(rdd)
+  }
+}
+
+class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  final val k = 5
+  @transient var dataset: DataFrame = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+
+    dataset = KMeansSuite.generateKMeansData(sqlContext, 50, 3, k)
+  }
+
+  test("default parameters") {
+    val kmeans = new KMeans()
+
+    assert(kmeans.getK === 2)
+    assert(kmeans.getFeaturesCol === "features")
+    assert(kmeans.getPredictionCol === "prediction")
+    assert(kmeans.getMaxIter === 20)
+    assert(kmeans.getRuns === 1)
+    assert(kmeans.getInitMode === MLlibKMeans.K_MEANS_PARALLEL)
+    assert(kmeans.getInitSteps === 5)
+    assert(kmeans.getEpsilon === 1e-4)
+  }
+
+  test("set parameters") {
+    val kmeans = new KMeans()
+      .setK(9)
+      .setFeaturesCol("test_feature")
+      .setPredictionCol("test_prediction")
+      .setMaxIter(33)
+      .setRuns(7)
+      .setInitMode(MLlibKMeans.RANDOM)
+      .setInitSteps(3)
+      .setSeed(123)
+      .setEpsilon(1e-3)
+
+    assert(kmeans.getK === 9)
+    assert(kmeans.getFeaturesCol === "test_feature")
+    assert(kmeans.getPredictionCol === "test_prediction")
+    assert(kmeans.getMaxIter === 33)
+    assert(kmeans.getRuns === 7)
+    assert(kmeans.getInitMode === MLlibKMeans.RANDOM)
+    assert(kmeans.getInitSteps === 3)
+    assert(kmeans.getSeed === 123)
+    assert(kmeans.getEpsilon === 1e-3)
+  }
+
+  test("parameters validation") {
+    intercept[IllegalArgumentException] {
+      new KMeans().setK(1)
+    }
+    intercept[IllegalArgumentException] {
+      new KMeans().setInitMode("no_such_a_mode")
+    }
+    intercept[IllegalArgumentException] {
+      new KMeans().setInitSteps(0)
+    }
+    intercept[IllegalArgumentException] {
+      new KMeans().setRuns(0)
+    }
+  }
+
+  test("fit & transform") {
+    val predictionColName = "kmeans_prediction"
+    val kmeans = new KMeans().setK(k).setPredictionCol(predictionColName).setSeed(1)
+    val model = kmeans.fit(dataset)
+    assert(model.clusterCenters.length === k)
+
+    val transformed = model.transform(dataset)
+    val expectedColumns = Array("features", predictionColName)
+    expectedColumns.foreach { column =>
+      assert(transformed.columns.contains(column))
+    }
+    val clusters = transformed.select(predictionColName).map(_.getInt(0)).distinct().collect().toSet
+    assert(clusters.size === k)
+    assert(clusters === Set(0, 1, 2, 3, 4))
+  }
+}
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 4291b0be2a616..12828547d7077 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -481,8 +481,8 @@ object Unidoc {
         "mllib.tree.impurity", "mllib.tree.model", "mllib.util",
         "mllib.evaluation", "mllib.feature", "mllib.random", "mllib.stat.correlation",
         "mllib.stat.test", "mllib.tree.impl", "mllib.tree.loss",
-        "ml", "ml.attribute", "ml.classification", "ml.evaluation", "ml.feature", "ml.param",
-        "ml.recommendation", "ml.regression", "ml.tuning"
+        "ml", "ml.attribute", "ml.classification", "ml.clustering", "ml.evaluation", "ml.feature",
+        "ml.param", "ml.recommendation", "ml.regression", "ml.tuning"
       ),
       "-group", "Spark SQL", packageList("sql.api.java", "sql.api.java.types", "sql.hive.api.java"),
       "-noqualifier", "java.lang"
diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst
index 518b8e774dd5f..86d4186a2c798 100644
--- a/python/docs/pyspark.ml.rst
+++ b/python/docs/pyspark.ml.rst
@@ -33,6 +33,14 @@ pyspark.ml.classification module
     :undoc-members:
     :inherited-members:
 
+pyspark.ml.clustering module
+----------------------------
+
+.. automodule:: pyspark.ml.clustering
+    :members:
+    :undoc-members:
+    :inherited-members:
+
 pyspark.ml.recommendation module
 --------------------------------
 
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
new file mode 100644
index 0000000000000..b5e9b6549d9f1
--- /dev/null
+++ b/python/pyspark/ml/clustering.py
@@ -0,0 +1,206 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark.ml.util import keyword_only
+from pyspark.ml.wrapper import JavaEstimator, JavaModel
+from pyspark.ml.param.shared import *
+from pyspark.mllib.common import inherit_doc
+from pyspark.mllib.linalg import _convert_to_vector
+
+__all__ = ['KMeans', 'KMeansModel']
+
+
+class KMeansModel(JavaModel):
+    """
+    Model fitted by KMeans.
+    """
+
+    def clusterCenters(self):
+        """Get the cluster centers, represented as a list of NumPy arrays."""
+        return [c.toArray() for c in self._call_java("clusterCenters")]
+
+
+@inherit_doc
+class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
+    """
+    K-means Clustering
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
+    ...         (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
+    >>> df = sqlContext.createDataFrame(data, ["features"])
+    >>> kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol("features")
+    >>> model = kmeans.fit(df)
+    >>> centers = model.clusterCenters()
+    >>> len(centers)
+    2
+    >>> transformed = model.transform(df).select("features", "prediction")
+    >>> rows = transformed.collect()
+    >>> rows[0].prediction == rows[1].prediction
+    True
+    >>> rows[2].prediction == rows[3].prediction
+    True
+    """
+
+    # a placeholder to make it appear in the generated doc
+    k = Param(Params._dummy(), "k", "number of clusters to create")
+    epsilon = Param(Params._dummy(), "epsilon",
+                    "distance threshold within which " +
+                    "we've consider centers to have converged")
+    runs = Param(Params._dummy(), "runs", "number of runs of the algorithm to execute in parallel")
+    initMode = Param(Params._dummy(), "initMode",
+                     "the initialization algorithm. This can be either \"random\" to " +
+                     "choose random points as initial cluster centers, or \"k-means||\" " +
+                     "to use a parallel variant of k-means++")
+    initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode")
+
+    @keyword_only
+    def __init__(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initStep=5):
+        super(KMeans, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.KMeans", self.uid)
+        self.k = Param(self, "k", "number of clusters to create")
+        self.epsilon = Param(self, "epsilon",
+                             "distance threshold within which " +
+                             "we've consider centers to have converged")
+        self.runs = Param(self, "runs", "number of runs of the algorithm to execute in parallel")
+        self.seed = Param(self, "seed", "random seed")
+        self.initMode = Param(self, "initMode",
+                              "the initialization algorithm. This can be either \"random\" to " +
+                              "choose random points as initial cluster centers, or \"k-means||\" " +
+                              "to use a parallel variant of k-means++")
+        self.initSteps = Param(self, "initSteps", "steps for k-means initialization mode")
+        self._setDefault(k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    def _create_model(self, java_model):
+        return KMeansModel(java_model)
+
+    @keyword_only
+    def setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
+        """
+        setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
+
+        Sets params for KMeans.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setK(self, value):
+        """
+        Sets the value of :py:attr:`k`.
+
+        >>> algo = KMeans().setK(10)
+        >>> algo.getK()
+        10
+        """
+        self._paramMap[self.k] = value
+        return self
+
+    def getK(self):
+        """
+        Gets the value of `k`
+        """
+        return self.getOrDefault(self.k)
+
+    def setEpsilon(self, value):
+        """
+        Sets the value of :py:attr:`epsilon`.
+
+        >>> algo = KMeans().setEpsilon(1e-5)
+        >>> abs(algo.getEpsilon() - 1e-5) < 1e-5
+        True
+        """
+        self._paramMap[self.epsilon] = value
+        return self
+
+    def getEpsilon(self):
+        """
+        Gets the value of `epsilon`
+        """
+        return self.getOrDefault(self.epsilon)
+
+    def setRuns(self, value):
+        """
+        Sets the value of :py:attr:`runs`.
+
+        >>> algo = KMeans().setRuns(10)
+        >>> algo.getRuns()
+        10
+        """
+        self._paramMap[self.runs] = value
+        return self
+
+    def getRuns(self):
+        """
+        Gets the value of `runs`
+        """
+        return self.getOrDefault(self.runs)
+
+    def setInitMode(self, value):
+        """
+        Sets the value of :py:attr:`initMode`.
+
+        >>> algo = KMeans()
+        >>> algo.getInitMode()
+        'k-means||'
+        >>> algo = algo.setInitMode("random")
+        >>> algo.getInitMode()
+        'random'
+        """
+        self._paramMap[self.initMode] = value
+        return self
+
+    def getInitMode(self):
+        """
+        Gets the value of `initMode`
+        """
+        return self.getOrDefault(self.initMode)
+
+    def setInitSteps(self, value):
+        """
+        Sets the value of :py:attr:`initSteps`.
+
+        >>> algo = KMeans().setInitSteps(10)
+        >>> algo.getInitSteps()
+        10
+        """
+        self._paramMap[self.initSteps] = value
+        return self
+
+    def getInitSteps(self):
+        """
+        Gets the value of `initSteps`
+        """
+        return self.getOrDefault(self.initSteps)
+
+
+if __name__ == "__main__":
+    import doctest
+    from pyspark.context import SparkContext
+    from pyspark.sql import SQLContext
+    globs = globals().copy()
+    # The small batch size here ensures that we see multiple batches,
+    # even in these small test examples:
+    sc = SparkContext("local[2]", "ml.clustering tests")
+    sqlContext = SQLContext(sc)
+    globs['sc'] = sc
+    globs['sqlContext'] = sqlContext
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
+    sc.stop()
+    if failure_count:
+        exit(-1)